Method and apparatus for improving manufacturing processes

ABSTRACT

The invention determines the batch size of materials required for each process within a workstation based on a given shipment schedule, as well as the values of several other workstation variables that are determinative of workstation and factory performance. With this information, the user of the invention may schedule production for the factory or spot and prioritize workstations requiring the most improvement, and determine the character and quantity of improvement.

FIELD OF THE INVENTION

This invention relates generally to modeling systems for manufacturingprocesses, and more particularly to a method and apparatus for analyzingmanufacturing processes in order to schedule production and improve themanufacturing performance of a factory.

BACKGROUND OF THE INVENTION

In the global economy, domestic manufacturing enterprises are facingformidable competition from foreign companies who offer high qualitygoods at low prices. Domestic enterprises cannot remain competitive withforeign or even domestic companies by manufacturing goods in accordancewith conventional practices. The intense present day competitionnecessitates rapid and, indeed, continual improvement of the methods andfacilities for manufacturing goods.

There is a bewildering array of technological innovations available toenable a company to improve its manufacturing processes. An enterprisemay choose to automate its factory with robots, MPR and/or computerintegrated manufacturing. It may often reduce direct labor costs andimprove employee performance through motivational techniques.Improvements involving automation, however, are likely to be veryexpensive and to demonstrably affect the continued viability of theenterprise in a competitive market. Thereafter, accurate analysis ofmanufacturing processes and investment in improving existing processesis critical to performance. Difficult questions have to be asked andanswered about what is to be improved, how to improve it, and in whatorder to make the improvements for the optimal cost-efficientperformance.

Unfortunately, decision makers often rely more on intuition than on anaccurate analysis. Reliance on intuition, more often than not, proves tobe misleading. Intuition is often misguided by outdated beliefs ormisunderstandings of the principles of manufacturing. Very oftenintuition does not lead to lower costs or higher quality. Indeed, theseolder manufacturing Principles may have the reverse effect. To cite oneexample, most American manufacturers use a batch production method inwhich batch sizes for a manufacturing process are increased so as toreduce the direct manufacturing cost per unit. Contrary to intuition,however, running larger batch sizes can actually increase indirectmanufacturing costs and conceal waste functions that are likely toimpede efforts to lower manufacturing costs and improve the productquality. Waste functions may include, for example, excessively longset-up times for each manufacturing process, the amount of scrapproduced by a process, the amount of rework that is done, the effect ofmachine and human down time. These waste functions necessitate, for agiven volume of material, more labor, more inventory, more capitalequipment, more time, and more physical space. Thus, overhead, plant andcapital costs are increased with batch manufacturing. Furthermore,increased batch sizes inevitably affect quality negatively. Finally,running large batch sizes makes it more costly to build custom productsthat many markets demand, decreases responsiveness to changing marketconditions, and slows the introduction of new products.

The executive tasked with improving the manufacturing processes of anenterprise must therefore ignore intuition and seek guidance forimproving the manufacturing process with sound manufacturing principles.

One approach that overcomes misguided intuition is the "Just In Time" or"Toyota" method. The basic tenant of "Just In Time" is that an existingshipment or factory output schedule should be met with ever smallerbatch sizes of the raw materials and intermediate products that make upthe final product. With "Just In Time", batch sizes are madeincreasingly smaller until a particular workstation fails. Appropriateadjustments are then made in the manufacturing process. The "Just InTime" method replaces misguided intuition by basing improvements onreduction of batch sizes.

Although superior to intuition, "Just In Time" has its drawbacks.Improvements to a factory using the "Just In Time" method are madeslowly and can result in temporary but sometimes lengthy halts inproduction. Since batch size reductions are necessary to gatherinformation on which processes in the factory require the mostimprovement, the method is only suitable for the manufacture of productsin large lots, such as automobiles. Many enterprises lack the time,money or volume of production to make them suitable candidates forimprovement by the "Just In Time" method.

A tool with which to analyze a manufacturing process before making thechanges to the processes is therefore required for those not able to use"Just In Time". Indeed, even those using "Just In Time" will benefitfrom this sort of tool.

One type of prior art tool is one that dynamically models the real-timeoperation of a factory. Modeling languages such as SLAM and GPSS havebeen successfully used to model manufacturing processes. Successful useof these languages, however, requires expert computer programmingskills. Normally, those tasked with improving the manufacturing processare manufacturing executives and engineers, not expert programmers, andlack the skills necessary to apply the modeling language techniques totheir particular processes.

Moreover, dynamic simulation tools suffer from other significantshortcomings. The accuracy of the program's simulation is limited by themodeler's insight and understanding of the manufacturing process. Thesereal-time models merely simulate the movement of material through thevarious manufacturing processes by monitoring the size of the queues ofmaterial at various points in the factory. Apart from showing that aprocess in the factory has either too much material or not enoughmaterial to process, the size of the queues of material waiting to beprocessed do not provide information useful in determining whatcomponent of the manufacturing process should be improved or how toimprove it. Multiple hypothetical runs must be made to see what effect agiven set of parameter changes will have on performance. Informationabout what changes in the process will yield the most significantimprovement therefore must be discovered by trial and error. With a verylarge factory, in which multiple processes are running simultaneously,the use of such programs to simulate real-time production is sodifficult that it is almost impossible to predict the effects of changesin a manufacturing processes.

To improve the efficiency of manufacturing processes in a factory, ananalytical tool simple and easy enough to be used by non-expertprogrammers is needed for accurately modelling the manufacturingprocess, identifying the steps or processes which are candidates forimprovement, prioritizing the candidates for improvement, anddetermining the character and quantity of improvement.

SUMMARY OF THE INVENTION

This invention is a tool to be used for planning improvements to amanufacturing facility. The apparatus of the preferred embodiment is aspecially programmed digital computer. The method of the invention is aseries of steps to be implemented, in the preferred embodiment, with adigital computer.

Unlike most other prior art models of manufacturing processes, thisinvention does not dynamically simulate the running of the manufacturingprocesses. Instead, it breaks down the factory into flows of materialgoing into and out of each workstation in the factory for each processtaking place within a workstation. Each process has associated with itan effective set-up time and an effective processing time per unit ofmaterial. The total time that it takes a workstation to set-up eachprocess and manufacture a predetermined batch of materials for everyprocess at that workstation is called workstation cycle time. With thebatch size for each process and cycle time for each workstation, as wellas other values determined by the invention, production may be scheduledand necessary improvements to the factory determined.

Minimum flow rates required to meet given outputs of the factory arefirst determined by the invention. With the effective set-up times foreach process and processing times per unit of material, batch sizes foreach process and the workstation cycle times necessary to meet therequired flow rates can be determined. The invention also determines theeffective processing times and the effective set-up times. The effectiveset-up times and the effective processing times are affected by scrapand rework generated by the process and start-up scrap and start-uprework and other factors which cause the workstation to be down. Theinvention is capable of modelling a factory having at least one and asmany as hundreds of workstations and directly determines batch sizes andworkstation cycle times, as well as other values of variables indicativeof the performance of the manufacturing processes within the factory.These values then serve to guide the decision maker on improving themanufacturing processes for lower cost and higher quality manufacturing.

The invention requires information concerning all of the workstations inthe factory, the processes within each workstation, the inputs andoutputs of each Process, the shipping rates of the manufactured units,set-up time for each process and processing time per unit of materialfor each process. The invention may also be provided with the followingvalues, although they are not necessary for the model to run: thepercentage of scrap generated by each process; the percentage of reworkgenerated by each process; the rework set-up time; the rework processingtime per part or unit of measure; start-up scrap of each process;start-up rework of each process; the mean time between failures of eachprocess; the mean time between repairs of each process; the mean timebetween breaks for human workers for each process; the mean time onbreak for human workers for each process; the transport and queue timeto the next process; the dollar value of the parts or materialsgenerated by each process; the percent of rework done elsewhere for theparts or units generated by each process; and the batch size of parts orunits built for each process.

The invention analyzes the manufacturing processes in two ways. First,the schedule analysis determines from all the data provided to it theminimum allowable batch sizes for each process, and other valuesrelating to workstation performance, to be used to meet the shippingrates provided by the user. Second, capacity analysis determines fromthe data provided to it the minimum allowable batch sizes and the otherrelated values based on the factory running at peak capacity andbuilding products in the same ratios that were entered as shippingrates. The capacity analysis automatically adjusts the material flows inthe factory to run at peak capacity and shows the user whichworkstation(s) are limiting the capacity of the factory. At theconclusion of each analysis, the user is provided by the invention withvalues which can be used to prioritize the workstations and theprocesses in need of the most improvement, to determine how much theyneed to be improved, and to determine what types of improvements willresult in lower manufacturing costs and higher quality products. Thisdata includes batch sizes of materials for each process, the cycle timesfor each workstation, percentages of time spent setting up, processing,down, and idle for each workstation the amount of trapped inventory ateach workstation, the value of the scrap at each workstation, the valueof each batch at each workstation, the value of the trapped inventory ateach workstation, and the manufacturing cycle time through the worstcase path for every product shipped.

Thus, the invention serves as a tool to be used for improving theperformance of a manufacturing facility. Further, it is easilyapplicable to any factory having from one to hundreds of workstations byany user with a minimal amount of training. Because of its elegantlysimple solution to a very complex problem, the invention is capable ofbeing practiced even with a personal computer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a digital computer.

FIGS. 2a, 2b and 2c constitute a flow diagram of the steps of thepresent invention as carried out on a digital computer.

FIG. 3 is a flow diagram of one of the subroutines in FIG. 2 fordetermining the effective processing time per part for each process andthe effective set-up time for each process.

FIG. 4 is a flow diagram of a subroutine in the invention thatdetermines the percentages of time the workstation is setting up, idle,down, and processing for workstations having capacity variables greaterthan or equal to one (when the workstation is overcapacity).

FIG. 5 is a flow diagram for a subroutine in the capacity analysisbranch of the invention that determines which workstation(s) have thelargest capacity variable and sets the batch sizes if no set up time ispresent.

FIG. 6 is a flow diagram of another subroutine in the capacity analysisfor redetermining material flow rates and capacity variables.

FIGS. 7a and 7b constitute a flow diagram for the subroutine thatdetermines the minimum batch size required for each process, theworkstation cycle time for each workstation, and the values for othervariables demonstrating workstation performance.

FIG. 8 is a flow diagram for the subroutine that determines for eachprocess at each workstation the amount of trapped inventory, the valueof the batches of material, the value of the trapped inventory trappedat each workstation, and the value of the scrap generated by eachprocess.

FIG. 9 is a flow diagram for the subroutine that determines themanufacturing cycle time of the factory.

FIG. 10 shows an example of a model factory diagram for scheduleanalysis.

FIGS. 11 through 13 show examples of data provided by the user to theinvention of the model factory.

FIG. 14 is an example of a bill of material for a process within aworkstation of the model factory.

FIG. 15 is an example of the shipping rates entered by the user for eachproduct shipped by the model factory.

FIG. 16 is an example showing the batch sizes, batch values and scrapvalues determined by the invention for each process in the modelfactory.

FIG. 17 is a graphical representation of the sums of the batch sizes foreach station in the model factory determined by the invention.

FIG. 18 is an example of the cycle times and percentage breakdown of thetime spent at each station in the model factory as determined by theinvention.

FIG. 19 is an example of the amount of each part trapped at each stationand the corresponding part values as determined by the invention.

FIG. 20 is an example of the manufacturing cycle times as determined bythe invention for each part shipped.

FIG. 21 is the worst-case path through the factory that is used by theinvention to determine the manufacturing cycle time for one of the partsthat is shipped.

FIG. 22 shows an example of a model factory diagram for an improvedfactory.

FIGS. 23 through 25 show examples of data provided by the user to theinvention of the improved model factory.

FIG. 26 is an example of a bill of material for a process within aworkstation of the improved model factory.

FIG. 27 is an example of the shipping rates entered by the user for eachproduct shipped by the improved model factory.

FIG. 28 is an example showing the batch sizes, batch values and scrapvalues determined by the invention for each process in the improvedmodel factory.

FIG. 29 is a graphical representation of the sums of the batch sizes foreach station in the improved model factory determined by the invention.

FIG. 30 is an example of the cycle times and percentage breakdown of thetime spent at each station in the improved model factory as determinedby the invention.

FIG. 31 is an example of the amount of each part trapped at each stationand the corresponding part value in the improved model factory asdetermined by the invention.

FIG. 32 is an example of the manufacturing cycle times as determined bythe invention for each part shipped in the improved model factory.

FIG. 33 is the worst-case path through the improved model factory thatis used by the invention to determine the manufacturing cycle time forone of the parts that is shipped.

DETAILED DESCRIPTION OF THE DRAWINGS

In the preferred embodiment, the apparatus of this invention is aspecially programmed data processing system. The steps of the method ofthis invention are performable by a data processing system.

Referring to FIG. 1, the hardware of the preferred embodiment is a dataprocessing system having a microprocessor 102 and a random access memorydevice (RAM) 104 for storing data and software.

A user interface device 106 is connected to the microprocessor 102 andmemory device 104 through which the user may enter and receive data. Inthe preferred embodiment, the user interface device 106 is a videodisplay monitor with an alphanumeric keyboard. The interface device mayalso include a printer. A data storage device 108 for storing software,factory models, and other information for implementing the invention islikewise connected to microprocessor 102 and memory 104. In preferredembodiment permanent data storage device 108 is a hard disk drive orother comparable device such as those employing magnetic or opticalmedia. A floppy disk drive should also be available to initially installthe software.

Data processing system 110 can be any digital processor having adequateprocessing speed and memory. A personal computer having a microprocessoroperating at 10 to 33 MHz and a storage capacity of 40 Megabytes areexemplary. Many commercially available "Laptop" computers are likewisesuitable.

A description of the theoretical model and of its nomenclature whichforms the basis for the present invention will be helpful to anunderstanding of the invention. A factory has one or more workstations,each of which carries out one or more processes. The factory alsoincludes receiving, shipping and possibly quality control stations.Every process has at least one input and output. Material to beprocessed originates at a receiving station. An input for a processarrives from either a receiving station, another workstation or aquality control station. The outputs of each process may be routed toanother workstation, a quality control station or shipping station. Thefinal factory outputs are all sent to the shipping station(s), wherethey are shipped at known shipping rates for the schedule analysis andat calculated shipping rates for the capacity analysis.

Each process at a workstation processes a particular type of material.The average time that it takes for the process to process one unit ofmaterial/is defined as the processing time per unit of material (P). Theaverage processing time is generally taken while the worker is present,the machine is running and when rework is not required. A unit can be adiscrete part or a unit of measure of the material. When a workstationhas more than one process, the end of one process may require the nextprocess to be set up. The set up time (SU) for the second process is thetime between the last good unit produced by the first process and thefirst good unit produced by the second process less the processing timefor one unit of the second part. Set up time is generally taken to bethe average time to set up the process when the workers are present andthe equipment is operational. Set-up produces only bad units ordefective material if material is produced during set-up. Set-upmaterial may be scrapped, the amount of scrap being referred to asset-up scrap (SUSCRP). It may also be reworked into good material, thenumber of units reworked being referred to as set-up rework (SUREWK).Each process may also produce a certain amount of defective materialafter set-up and during processing. Some of this material may bereworked and made into good material. The percentage of material to bereworked from each process is known as percent rework (%REWK). Therework may be performed at that workstation or elsewhere. The percent ofparts reworked elsewhere is known as percent rework elsewhere (%REWKEL).Performing rework may require additional set-up time, known as reworkset-up time (RSU). Rework can be also done at a different processingrate. The rework processing time (RP) equals the average processing timeto rework a unit of material while the worker is present and the machineis operational. Those parts which were too defective to be reworked arescrapped. The percentage of parts for a given Process that are scrappedis known as percent scrap (%SCRP).

Workstations usually do not operate continually. When the workstationhas material to process but it is not operating, it is due to eithermachine failure or human break time. The average time between machinefailures is called mean time between failures (MTBF). The average timeit takes to repair a workstation experiencing a machine failure is themean time to repair (MTTR). Similarly, when a workstation whoseoperation depends on a human being cannot process units of materialbecause of the absence of the human being, there is human down time. Thehuman's absence is usually due to a break taken at specified intervalsfor a certain amount of time or for other functions that the operatormust do that are not directly related to manufacturing. The average timebetween breaks is called mean time to break (MTTB), and the average timeon break is called mean time on break (MTOB).

Once material has been processed by a workstation and is ready to besent to a subsequent station, it must be transported. The materialoften, however, must wait to be transported to the next process. This isknown as queue time. Transport and queue time are collected into asingle variable for each process called transport and queue time (TPQT).

A list of the above variables and their definitions is provided inAppendix I.

With this model of the factory, the invention quickly provides to theuser values of a number of variables with which the user may improve theperformance of the factory. FIG. 2, a flow diagram of the subroutinescomprising the application software of the present invention, shows thegeneral method with which the present invention analyzes the abovefactory model. Details of the method are shown in the flow diagrams ofselected subroutines shown in FIGS. 3 to 9.

Referring to FIG. 2, the invention begins with block 202. The dataprocessing system 110 is provided by the user with factory data throughuser interface 106. The data is stored in memory device 104 andpermanent storage device 108. Factory data includes information on allworkstations, receiving stations, quality control stations, shippingstations, and all processes for each workstation and quality controlstation. Inputs and outputs for each process must also be provided. Thisinformation is entered by the user via a keyboard on an interactivebasis. Of course other means may be used to provide the information tothe digital processor such as magnetic tape, magnetic diskettes,magnetic hard disks, optical storage devices, or any other digitalstorage media. Indeed, once a factory model is created from this data,it may be stored in and later retrieved from the permanent storagedevice.

Data which must be provided to the digital processor are the processingtime per unit of material and the set-up time for each process. Valuesfor the following variables may, but do not necessarily have to beentered:

Percent scrap (%SCRP)

Percent rework (%REWK)

Rework set-up time (RSU)

Rework processing time (RP)

Start-up scrap (SUSCRP)

Start-up rework (SUREWK)

Mean time between failures (MTBF)

Mean time to repair (MTTR)

Mean time to break (MTTB)

Mean time on break (MTOB)

Transport and queue time to next process (TPQT)

Dollar value of material (VALUE)

Percent rework elsewhere (%REWKEL)

Manual batch size (MANBAT)

Two additional variables not previously defined are the dollar value ofmaterial (VALUE), being the dollar value of each part or unit ofmaterial passing through a process, and the manual batch size ofmaterial (MANBAT) for a process The latter provides the opportunity forthe user to enter batch sizes of the materials actually to be used foreach process instead of determining them from shipping rates.

The next step, shown in block 204, is performed for every process ateach workstation of the factory, and executes a subroutine to computethe effective processing time per part (Peff) and the effective set-uptime (SUeff) per process for every process. The following example anddiagram is helpful to understanding Peff and SUeff. For a workstationperforming two processes, A and B, its work cycle may be diagramed alonga time axis with "1" representing a unit of material processed and "--"representing set up time: ##STR1##

The time required of a process to build one unit of material is equal toP. Peff for process "A", on the other hand, is the time period T2divided by number of good units of "A" built. If some of "A" that isbuilt is defective and must be scraped or reworked, the number of good"A" falls and Peff grows longer. Similarly, if the machine breaks duringthe period T2 or human beings necessary to operate the workstation go onbreak, T2 is longer and Peff increases. Period T3 represents the time toset up to rework defective "A's". Period T4 represents the time torework defective material produced by the processing of "A" into goodmaterial. The time to rework a unit of material is RP. Sincereprocessing produces good material it is also included with theprocessing time. For each unit of "A" that is reworked, RP must be addedto the total processing time. The effect of rework on Peff must be takeninto account when determining Peff.

Until a good unit of "A" is produced, the workstation is said to besetting up. Set up includes by definition, therefore, the time toproduce the defective parts that may be incidental to setting up aprocess. Similarly, by definition, set up time is only affected bymachine down time when the machine is producing either start-up scrap orstart-up rework since the machine is not running during the rest ofset-up. Human downtime, however, directly affects set up time andprocessing time; the absence of a human being required to set up orprocess parts lengthens set up time or processing time. If to reworkmaterial from process "A" requires additional set up, the rework set uptime (RSU) period T3 shown in the above diagram for process "A" willeffect SUeff for process "A". Details of the computation of Peff andSUeff are described in connection with FIG. 3.

The next step represented by block 206 of FIG. 2, executes a subroutinein the software for determining the flow rate of material into and outof each process at each workstation that is necessary to meet the givenshipping rates. ("flow" refers to the movement of both liquid and solidmaterial.) By the principle of conservation of matter, the amount ofmaterial supplied to a process must equal the amount of material outplus scrap. The flow rate into a process (FLOWIN) at a workstation isrelated, therefore, to the flow rate out (FLOWOUT) by the followingequation:

    FLOWIN.sub.ij =[FLOWOUT.sub.ij ][1/(1/-%SCRP.sub.ij)].     (1)

Beginning with a receiving station and ending with a shipping station,material passes through various workstations and forms one or morepaths. With equation (1), the input flow rate for each process withineach workstation necessary to generate an output flow rate to meetshipping rates at the shipping stations are found by tracing all thepaths that all the material, and their components, took through thefactory. FLOWIN_(ij) and FLOWOUT_(ij) for each process i withinworkstation j are thus found.

The next step, described by block 208, is a loop for determining foreach workstation a capacity variable (CP). The capacity variable for aworkstation is the sum of the effective processing times per unit ofmaterial for each process at the workstation multiplied by the outputflow rate required by that process:

    CP.sub.j =(Peff.sub.ij) (FLOWOUT.sub.ij)                   (2)

The capacity variable is a measure of the capacity of the workstationsto process all of the material necessary to meet the flow raterequirements for each process at the workstation. A workstation, forexample, having a capacity variable equal to one is exactly at itscapacity while a workstation having a capacity variable greater than oneis over its capacity. In either of the above cases, the workstation willhave only down time and processing time. These values are thereforedetermined. The workstation is, consequently, never idle and never hastime to set up. These variables are therefore set equal to zero for theworkstation.

Depending on what type of analysis the user has chosen, the next step ofthe data processing system is to proceed at decision point 210 to eithera schedule analysis branch 211 or to a capacity analysis branch 212.Schedule and capacity analysis differ primarily in the flow rates usedto analyze workstation performance. Schedule analysis is chosen by theuser who wants to analyze the factory when it is producing a mix ofmaterials to meet the entered shipping rates. With this analysis, theinvention will provide the user with the actual batch size of materialsrequired to meet a shipment schedule. With capacity analysis, the useranalyzes the performance of the factory when it is processing at itscapacity the same mix of materials but in the largest quantitiespossible. The user may use capacity analysis to determine whichworkstation(s) of the factory limits the maximum size of the factoryoutput for a given mix of parts shipped.

Considering schedule analysis first, the schedule analysis subroutinestarts at block 213 and is performed for each workstation having acapacity variable greater than one or equal to one if one or moreprocesses have set up. These workstations have exceeded their limit forprocessing material at the output flow rates determined by thesubroutine of block 206. This is indicated on the user interface of thedata processing system. Block 228 is performed for each workstationhaving a capacity variable equal to one and having processes that haveno set up time. These workstations have reached their maximum capacityfor processing material at the output flow rates determined by thesubroutine of block 206.

If the capacity analysis branch is taken, the subroutine represented byblock 214 looks for the workstations with the largest capacity variable.It is the workstation with the largest CP that limits the outputcapacity of the factory. The software sets the variable CP_(LG) equal tothis value. CP_(LG) is thus a ratio indicating the percentage of theprocessing capacity of the factory utilized to process material to meetthe entered shipping rates.

In block 230 all capacity variables are redetermined after beingadjusted to maximum capacity.

For the purposes of capacity analysis, those workstations with CP equalto CP_(LG) are treated as being at capacity, or having a CP equal toone. Therefore, just as the schedule analysis subroutine of blocks 213and 228 did for those workstations with capacity variables equal to one,the next subroutine of the capacity analysis, block 215, indicates that,for each workstation with a capacity variable equal to the value ofCP_(LG), the capacity of the workstation has been reached, which limitis indicated on both the batch size and the cycle time.

The next step within the capacity analysis branch 212, represented byblock 216, adjusts the input and output flow rates of each process ateach workstation to those of a factory running at its capacity. CP_(lg)is the proportionately constant which relates flow rates determined tomeet the entered shipping rates and the flow rates for a factory runningat capacity. Therefore, the input and output flow rates for each processat each workstation determined by subroutine 206 can be easily adjustedto capacity flow rates by dividing each flow rate with CP_(LG).

Thereafter the schedule analysis branch 211 and the capacity analysisbranch 212 share common subroutines, beginning with the subroutinerepresented by block 218. The two analyses differ only in the flow ratesused for each process. This subroutine determines for each workstationhaving a capacity variable less than one, the minimum allowable batchsize for each process to meet the flow rates required of that process oruses the user supplied batch size if it is provided. The flow rates usedfor this computation are those determined by subroutine 206 for scheduleanalysis, or those determined by subroutine 216 for capacity analysis.

The next subroutine, represented by block 219, determines station cycletimes for each workstation based on the batch sizes provided by block218.

The next subroutine, represented by block 220, sets the variable %TOTALequal to the value of one for each workstation with a capacity variableless than or equal to one. A workstation, by definition, must be eitherdown, processing, or setting up. When not in one of those three states,it is idle. %TOTAL for a workstation is therefore equal to the sum ofset-up time, processing time, down time and idle time for theworkstation. By definition, a workstation at or below capacity spendsall of its time in one of these four states. %SETUP, %PROC, %DOWN, and%IDLE are all determined at this point.

Those workstations having a capacity variable greater than or equal toone cannot, however, have set-up time or idle time. For workstationswith a capacity variable greater than one, the subroutine represented byblock 222 sets the %TOTAL variable equal to the percent of down timeplus the percent of processing time which will equal more than 100%.SETUP and %IDLE are therefore set equal to zero, and %PROC and %DOWN aredetermined.

The next subroutine in the software, represented by block 224,determines the value of the batches used by each process, the value ofthe scrap generated by each process, the amount of trapped inventory ateach process due to the transport and queue time to the next process forthe output of the process, and the value of the trapped inventory.

The next subroutine in the software, represented by block 225,determined the value of the Manufacturing Cycle Time (MCT) for each partthat is shipped. MCT indicates the path, having the longest duration,through the factory for each part that is shipped from the factory.

The final step, as represented by block 226, is to display all of theresults to the user. The following values are displayed both in tabularform and in graphical form: batch size for each process; cycle time foreach workstation; percent set-up time, percent down time, percent idletime, percent processing time, and percent total time for eachworkstation; batch value, scrap value, amount of trapped inventory,trapped inventory value for each process; and Manufacturing Cycle Timefor each part shipped from the factory.

The user may use these values provided by the invention, such as batchsize, to determine the optimal conditions for running the currentfactory and to schedule production. The user may also use them to makeimprovements to the manufacturing system. For example, workstations withthe longest cycle times are the greatest obstacles to low cost, highquality manufacturing. Improvements are also prioritized. Theworkstation with the longest cycle time or the largest batch sizesrequire the most improvement. How best to improve the workstation canalso be determined by the user. If the long cycle times are caused bylong processing times, for example, some sort of automation or roboticsmay be the best mode for improvement to reduce actual processing timeper part. If, on the other hand, the slow cycle time is caused by longset-up times, rapid changeover is the best method of improvement. Theinvention thus provides an analytical tool with which to make informeddecisions about manufacturing processes.

FIGS. 3 through 9 illustrate the detailed steps for certain of thesubroutines discussed in connection with FIG. 2.

Referring first to FIG. 3, there is shown the detailed flow diagram ofthe subroutine represented by block 204 of FIG. 2. This subroutinedetermines the effective processing time per part and the effectiveset-up time for each process at each workstation. The subroutine isiteratively executed as a loop, and is performed, as shown by blocks 300and 301, for each process i, i 1 to n, within each workstation j, j=1 tom. As shown by decision symbol 302, the subroutine begins with thedecision of whether process i inside workstation j generates materialthat is to be reworked. If not, control passes to block 304, where itcauses the effective processing rate for process i of workstation j(Peff_(ij)) to be set equal to the processing rate (P_(ij)) entered bythe user. Similarly, the effective set-up time for process i atworkstation j (SUeff_(ij)) is set equal to set-up time entered by theuser (SU_(ij)). If there is rework, block 306 determines the effectiveprocessing time according to the following equation,

    Peff.sub.ij =P.sub.ij +(RP.sub.ij)(%REWK.sub.in)           (3)

where RP_(ij) is the rework processing time per part and %REWK_(in) isthe percentage of rework generated by that process. In block 308, theeffective set-up time for the process is determined according to thefollowing equation,

    SUeff.sub.in =SU.sub.ij +RSU.sub.ij                        (4)

where RSU is the rework set-up time.

At step 310, a determination is made whether the process generates anyscrap. If so, the effective processing rate is redetermined at step 312according to the following equation,

    Peff.sub.in =(Peff.sub.in)(1/(1-%SCRP.sub.ij))             (5)

where %SCRP_(in) is the percentage of scrap generated by process i inworkstation j. Effective set-up time need not be redetermined because itis not affected by scrap. By definition, no good parts are beinggenerated during the set-up of the process and any parts or materialsgenerated by the set-up of the process are scrapped or will be reworked.

After the effect of scrap is determined, a determination is made at step314 of whether the process has human down time. Human down time slowseffective processing time per unit and the effective rework processingtime per unit. Thus, if human down time is present, the effectiveprocessing time per unit must be redetermined to take into account theaffect of human down time as shown in block 316, according to thefollowing relationship:

    Peff.sub.in =(Peff.sub.in)((MTTB.sub.in +MTOB.sub.in)/MTTB.sub.ij) (6)

MTTB_(ij) is the mean time to break for process i inside workstation j,and MTOB_(ij) is the mean time on break for process i inside workstationj. Similarly, the effect of human down time on rework processing time(RP_(in)) is determined, as shown in block 316, according to thefollowing:

    RPeff.sub.in =(RP.sub.ij)((MTTB.sub.ij +MTOB.sub.ij)/MTTB.sub.ij). (7)

It should be noted that Peff need not be redetermined with RPeff_(ij) inplace of RP_(ij) in equation (3). The effect of human down time onRP_(ij) for the purpose of determining Peff_(ij) is done with equation(6). RPeff_(if) is determined at this stage because it will, as will beshown in connection with block 712 of FIG. 7, determine in part thecycle time CT_(j).

After block 316, the subroutine determines, as shown in block 318, theeffect of human down time on set-up time. The equation shown in block318 is identical in form to equations (6) and (7). If there is no humandown time, the value of the variables Peff_(ij) and SUeff_(is) remainunchanged and RPeff is set equal to RP_(ij) in block 315.

Proceeding to decision block 320, the presence of machine downtimerequires the recomputation of the effective processing time per unit,the effective rework processing time per unit and the effective set-uptime per process. If there is machine down time, the effect of machinedown time on rework processing times and processing times is determined,as shown in block 322.

The next step of the subroutine, block 324, determines the effect ofmachine down time on set-up time. It should be appreciated that, bydefinition, machine failure can occur only during processing. Set-uptime, technically, is not affected by machine downtime. However, inmodeling the factory, the time to built start-up scrap or start-uprework is allocated to the set-up time for the process. The time ittakes to build these start-up units is the number of start-up reworkunits (SUREWK) multiplied by the effective processing time per unit plusthe number of start-up scrap units (SUSCRP) multiplied by the effectiveprocessing time per unit. Since the effective processing time per unitis affected by machine down time, the presence of machine down timerequires the redetermination of the effective set-up time for theprocess. Hence, as shown in block 324, the effective set-up time isredetermined. The process in FIG. 3 is repeated for every process atevery workstation, in the factory at which point the subroutine ends.

FIG. 4 is a detailed flow diagram of the subroutine represented byblocks 213 and 228 of FIG. 2, which are part of the schedule analysisbranch 211 and blocks 220 and 222 of the invention. As shown in block400, the subroutine is iteratively executed for each workstation. Theloop begins by determining at step 402, whether the workstation j has acapacity variable greater than or equal to one. If so, the user isinformed at step 404 by an appropriate message on the user interfacethat the workstation is limited by its capacity. The batch sizes andcycle times are shown as being at "limit" if the sum of the set-up timesof all of the processes in the workstation, with flow rates greater thanzero, is greater than zero. Then, as indicated by block 405, if thecapacity variable is equal to one and the sum of the set-up times of allof the processes in the workstation with flow rates greater than zero isequal to zero, then the batch sizes of the required materials are setequal to one. Next, as indicated by a block 406, the subroutine setsequal to zero the variables for the percentage of time that theworkstation spends setting up (%SETUP) and the percentage of time thatthe workstation remains idle (%IDLE). A capacity variable equal to orgreater than one means that the cumulative demand on the workstationequals or exceeds its capacity. Therefore, by definition, there is noidle time and there is no time left for setting up between processes.The workstation must spend all available time processing units when itis not down due to machine down time or human down time.

Workstations with a capacity variable equal to or greater than one spendall of their time either processing or down. In order to determine thepercent of time a workstation is down, the subroutine illustrated inFIG. 4 determines the percentage of time that the workstation is up, orin other words, not down due to either human time or machine down time.The percentage of up time (%PROC_(j)) is determined by accounting forthe amount of human down time and machine down time. This determinationis done using the equation shown in block 408. %DOWN_(j) is related to%PROC_(j) by the following:

    %DOWN.sub.j =CP.sub.j -PROC.sub.j.                         (8)

This relation is, as shown in block 412, used to determine %DOWN_(j).The subroutine is repeated for each workstation.

Now referring to the capacity analysis branch 212 in the flow diagram ofFIG. 2, the details of the two subroutines shown by blocks 214 and 215will now be described.

Referring to FIG. 5, there is shown a detailed flow diagramcorresponding to the subroutines indicated by blocks 214 and 215 of FIG.2. Block 502 represents a first iteration performed for everyworkstation j, j=1 to m, that sets CP_(LG) equal to CP_(j) if CP_(j) isgreater than CP_(LG).

Beginning at block 504 a second iteration is performed for eachworkstation j, j=1 to m. The first step of the second iteration, shownby block 506, determines if CP_(j) equals CP_(LG). If it does not, thesubroutine returns to block 504. Otherwise, the next step, block 508,involves the determination of whether any set up time is present inworkstation j. If set up time is present for any process i that has aFLOWOUT_(ij) greater than zero, limit is indicated on the batch size(B_(ij)) as shown in block 518. A limit is also indicated on the cycletime (CT_(j)). If set up time is not present in workstation j then aloop is performed for process i=1 to n as indicated by block 510. Block512 checks to see if FLOWOUT_(ij) equals zero. If FLOWOUT_(ij) equalszero, then B_(ij) is set equal to zero as shown in block 516. IfFLOWOUT_(ij) is greater than zero then B_(ij) is set equal to one asshown in block 514 and it is indicated on the user interface that themaximum capacity has been reached. Block 517 completes the loop andtests to see if i equals n. If the condition i equals n is met, thenblock 520 tests to see if j equals m. If not the next workstation istested. When if j equals m the subrouting is completed.

As indicated by decision symbols 507 and 520, steps 504 to 518 arerepeated for the remaining workstations j, until j equals m, m being thenumber of workstations in the factory.

Turning now to FIG. 6, there is shown the flow diagram corresponding tosubroutines 216 and 230 of FIG. 2. The subroutine is repeated for eachworkstation, as indicated by block 602 and decision symbol 614. Withinthe loop for each workstation j there is nested a second loop performedfor each process i, as indicated at block 604 and decision symbol 610.As shown in blocks 606 and 608, flow rates for the process i atworkstation j are redetermined and set. FLOWOUT_(ij) is set in step 606equal to the old FLOWOUT_(ij), determined from the given shipmentschedule in subroutine 206 of FIG. 2, divided by CP_(LG). Similarly, instep 606, FLOWIN_(ij) is set in equal to the old FLOWIN_(ij), determinedfrom the subroutine 206 of FIG. 2, divided by CP_(LG). Steps 604, 606and 608 are repeated for each process within workstation j. Once the newflow rates for each process of a workstation are redetermined, a newcapacity variable for the workstation is determined based on these newflow rates. The equation for determined the capacity variable is shownin block 612, and is identical to equation (2). The entire subroutine isthen iteratively executed for every workstation in the factory.

FIG. 7, is a detailed flow diagram for the subroutine represented byblock 218 of FIG. 2. This iterative subroutine, as shown by block 702,is executed for each workstation. Furthermore, as shown in step 704, thesubroutine is performed only for workstations with capacity variablesless than one. If the capacity variable is equal to one, block 722 teststo see if the workstation has any set up time. If the workstation doesnot, the cycle time is determined for that workstation according to theequation in block 724.

The first step of the loop, shown in block 706, is to determine cycletime for the workstation. This equation, shown in block 706, can beshown to be derived from the following equation:

    CT.sub.j =[SUeff.sub.ij +(Peff.sub.ij) (B.sub.ij)]         (9)

As can be seen from equation (9), cycle time for a workstation is thesum of: the effective set-up times plus the product of the effectiveprocessing rate and the batch size for each process i within workstationj. As was described in connection with FIG. 3, step 324, the time toprocess start-up rework the first time and start-up scrap isincorporated into the effective set-up time. The equation in block 706is easily derived by substituting for B_(ij) in equation (9) with thefollowing equation and solving for CT_(j) :

    B.sub.ij =[(FLOWOUT.sub.ij (CT.sub.j)/(1-%SCRP.sub.ij)]+SUSCRP.sub.ij. (10)

After the cycle time for the workstation is determined, the batch sizefor each process is determined according to the equation contained inblock 708. The batch size calculated for each process is then, asdescribed in block 710, rounded up to the next highest whole number ifnot equal to a whole number. The steps in block 708 and the rounding upin block 710 must be performed for each process within workstation j.Steps 708 and 710 are therefore iteratively executed on each process.The steps of blocks 708 and 710 can be done in the same loop. Roundingup is necessary because a process will not produce a fraction of theunit of material; it must produce the entire unit. Requiring theworkstation to build that extra fraction of a unit for each process,however, lengthens the cycle time of the workstation. Therefore, asshown in block 712, the cycle time is redetermined using the directmethod of simply adding the components of time together now that thebatch size is known.

As shown in blocks 714, 716, 718 and 720, %SETUP_(j), %DOWN_(j).%PROC_(j) and %IDLE_(j) are determined. The equations with which todetermine the value of each of these variables are shown in therespective blocks.

After step 720, the subroutine is repeated, beginning with step 702, forevery workstation in the factory.

Going back to step 722, if the capacity variable is equal to one andthere is no set up time in the workstation for the mix of products beingpulled through that workstation, then, as shown in block of 405 of FIG.4, the batch size (B_(ij)) is equal to one. Block 724 then determinesthe cycle time of such a workstation by adding up all of the effectiveprocessing times (Peff_(ij)) for each process with a flow out(FLOWOUT_(ij)) that is greater than zero. Block 726 then tests to see ifall of the workstations have been tested. If so, the subroutine ends. Ifnot, the next workstation is tested.

Now turning to FIG. 8, there is shown the detailed flow diagram for thesubroutine represented by block 224 in FIG. 2. This subroutine, asindicated by block 802, is performed for each process i at a workstationj and for each workstation in the factory. First, as shown in step 804,the amount of trapped inventory (TPINV) at each process due to the timeto transport the units of material to the next process and the time theunit of material must wait in queue for the next process (collectivelycalled transport and queue time (TPQP)) is determined with the relationshown in the block.

Next as shown in block 806, the TPINV_(ij) value is multiplied by thevalue of the unit of material being processed (VALUE), to obtain atrapped inventory value (TPINV VALUE). The value of the batch size isalso determined as shown in block 708, by multiplying the batch size(B_(ij)) times the value of the units being processed (VALUE_(ij)).Finally, the value of the scrap generated by each process is determinedwith the equation in block 810. The subroutine then returns to block 802and repeats for every process within every workstation in the factory.

Finally, FIG. 9 shows the detailed flow diagram of the subroutine,represented by block 225 in FIG. 2 for determining the manufacturingcycle time (MCT_(is)). This subroutine, as indicated by blocks 902 and904, is performed for each process i at each shipping station S, and foreach shipping station in the factory. First, as shown in step 906, allpossible paths for each process from the shipping station back to areceiving station are determined. Next, as shown in block 908, thelength of time for parts to travel through each path is determined usingthe cycle times of each work station and the transport and que times ofeach work station along that path. The subroutine then returns to block904 and repeats for every process within every shipping station in thefactory.

Now referring to FIGS. 10 through 33, there is shown two examples of aschedule analysis performed by the invention on a model factory. FIGS.10 through 21 demonstrate a schedule analysis of the factory before anyimprovements are made to it. FIGS. 22 through 33 demonstrate a scheduleanalysis performed on the factory after improvement is made to thepercent rework of the processes "Solder P" and "Solder Q" in workstation"Solder".

Now referring to FIGS. 10 through 15, there is shown the data that isprovided by the user to the invention: in FIG. 10, a factory diagram; inFIGS. 11-13 the data for each process in the factory; in FIG. 14, anexample of the bill of material for the "Assembly P" process of thestation "assembly", which bill shows the amount of input material thatcomprises one unit of output material; and, in FIG. 15, the shippingrates for each product shipped. In FIGS. 16 through 21, there is shownthe information determined by the invention and provided to the user: inFIG. 16, the batch sizes, batch values and scrap values determined bythe invention for each process; in FIG. 17, a graphical representationof the sums of the batch sizes for each station determined by theinvention; in FIG. 18, the cycle times and percentage breakdown of thetime spent at each station; in FIG. 19, the amount of each part trappedat each station and the corresponding part values; in FIG. 20, themanufacturing cycle time for each part shipped; in FIG. 21, theworst-case path through the factory that is used to determine themanufacturing cycle time.

Based on the results of the first analysis the solder station, becauseit has longer cycle times and the largest batch sizes, is making thegreatest contribution to the cycle time. The solder station, more thanany other station, slows the movement of parts through the factory. Itis, therefore, the station most in need of improvement. Since areduction in the percent rework will result in a reduction in the batchsizes and cycle times for the quality control station (the secondstation most in need of improvement), percent rework is chosen as thebest mode of improvement.

Now referring to FIGS. 22 through 33 (FIGS. 22 to 33 correspond,respectively, to FIGS. 10 to 21), a second run of the schedule analysisprogram with a reduction in the percent rework from 30% to 27% isdemonstrated. The value of 27% was chosen to demonstrate that a smallreduction in the percent rework results in a dramatic improvement in thebatch sizes and cycle times for both the solder station and the qualitycontrol station. As shown in FIG. 28, the batch sizes for the solderstation were reduced to 124 units, and the batch sizes for the qualitycontrol station were reduced to 116 units--improvements of 47 and 33units, respectively. Correspondingly, the value of the batch sizes inboth the workstations also dropped; in quality control from a total of$75,990.00 to $59,160.00 and in solder, from a total of $82,080.00 to$59,520.00, representing significant cost savings to the factory. InFIGS. 30 and 32, there can be seen a significant improvement in cycletimes for each work station and of the manufacturing cycle times foreach part. The solder and quality control stations are no longer thechief causes of parts moving slowly through the factory.

With this information, the processes at the solder station can beimproved with engineering methods so as to reduce the percent reworkfrom 30% to 27%.

Other changes to the factory can also be quicky and easily tried todetermine the character and magnitude of the changes that result in theimprovements required.

Only the preferred embodiment of the invention has been described. Itshould be understood that the invention is not limited to theembodiments disclosed, but is intended to embrace any alternatives,modifications, rearrangements, or substitutions of parts or elements asfall within the spirit and scope of the invention.

    ______________________________________                                        APPENDIX I                                                                    VARIABLE    DEFINITION                                                        ______________________________________                                        j           Refers to the "jth" the workstation                               i           Refers to the "ith" process of a                                              particular workstation                                            P           The processing time per unit of material                          SU          Set-up time per process                                           Peff        Effective processing time per unit of                                         material                                                          SUeff       Effective set-up time per process                                 RSU         Rework set-up time                                                % REWK      Percentage of rework for a process                                RP          Processing time for a unit of material                                        to be reworked                                                    % SCRP      Percentage of scrap generated by a                                            process                                                           SUSCRP      The amount of scrap generated by the                                          set up of a process                                               MTTB        Mean time to break for a process (human)                          MTOB        Mean time on break for a process (human)                          MTBF        Mean time between failures for a                                              process (machine)                                                 MTTR        Mean time to repair for a process                                             (machine)                                                         SUREWK      Number of units of material for rework                                        generated by the set-up of a process                              FLOWIN      Input flow rate for a process required                                        to support an output flow rate                                    FLOWOUT     Output flow rate required of a process                            MCT         Manufacturing cycle time through the                                          factory                                                           CP          A capacity variable for a workstation                             % SETUP     Percent of time a workstation spends                                          setting up                                                        % IDLE      Percent of time a workstation is idle                             % DOWN      Percent of time a workstation is down                                         due to human down time or machine                                             downtime                                                          % PROC      Percent of time a workstation is                                              actually manufacturing good material                              B           Minimum batch size required for a                                             process to produce requested flow rates                           CT          The cycle time for a workstation                                  % TOTAL     Equals % SETUP + % DOWN +                                                     % PROC + % IDLE for a workstation                                 TPINV       Trapped inventory at a process                                    TPQT        Transport and queue time at the output                                        of a process                                                      VALUE       Value in dollars of a unit at a process                           BATCH VALUE Value in dollars of a batch of material                                       to be processed by a process                                      SCRAP VALUE Value in dollars of scrap generated for                                       a process                                                         TPINV VALUE Value in dollars of the inventory of                                          units trapped at a process                                        % REWKEL    Percentage of rework that is done at a                                        different workstation                                             MANBAT      Manual batch size to be used instead of                                       allowing them to be calculated based on                                       shipping rates                                                    ______________________________________                                    

I claim:
 1. A method of scheduling and operating production of a factoryhaving a plurality of work stations, each work station performing one ormore processes, in order to meet a predetermined shipping schedule,comprising the steps of:(a) determining the rate of material flow out(FLOWOUT_(ij)) of each process within each workstation of a factorynecessary for the factory to meet a predetermined shipping schedule; (b)determining the size of the batch of material for each process of eachworkstation necessary to meet each material flow rate determined in step(a) for the workstation; and (c) operating each process at eachworkstation with the batch sizes determined in step (b).
 2. The methodfor scheduling and operating production of a factory as set forth inclaim 1 wherein the size of the batch of material for process i atworkstation j is determined in step (b) substantially in accordance withthe following relationship:

    B.sub.ij =[(FLOWOUT.sub.ij)(CT.sub.j)/(1-%SCRP.sub.ij)]+SUSCRP.sub.ij ;

where, for process i within workstation j, B_(ij) is the batch size,FLOWOUT_(ij) is the rate of material flow out of the process in theworkstation necessary for the factory to meet a predetermined shippingschedule, CT_(j) is the cycle time of the workstation, %SCRP_(ij) is thepercentage of scrapped material produced by the process and SUSCRP_(ij)is the amount of scrapped material produced by the set-up of theprocess.
 3. The method for scheduling the operating production of afactory as set forth in claim 2 wherein CT_(j) is determined based onthe based upon the following relationship:

    CT.sub.j =[ΣSUeff.sub.ij ]/[1-Σ(Peff.sub.ij)(FLOWOUT.sub.ij)]

where, for process i within workstation j, SUeff_(ij) is the effectiveset-up time, Peff_(ij) is effective time to process one unit ofmaterial, FLOWOUT_(ij) is the material output flow rate, and n is thenumber of processes performed by workstation j.
 4. The method forscheduling and operating production according to claim 3 furthercomprising the steps of:redetermining Peff_(ij) if the process iproduces rework according to,

    Peff.sub.ij =P.sub.ij +(RP.sub.ij)(%REWK.sub.ij),

where RP_(ij) is the rework processing rate and %REWK_(i) is the AMOUNTpercent of material reworked; and redetermining Peff_(ij) if process iwithin workstation j produces scrap according to,

    Peff.sub.ij =(Peff.sub.ij)(1/(1-%SCRP.sub.ij)),

where %SCRP_(ij) is the percentage of scrap.
 5. The method forscheduling and operation production according to claim 3 furthercomprising the steps of:redetermining Peff_(ij) if process i withinworkstation j has associated with it human down time according to,

    Peff.sub.ij =(Peff.sub.ij)([MTTB.sub.ij +MTOB.sub.ij ]/MTTB.sub.ij),

where MTTB_(ij) equals the mean time between human breaks and MTOB_(ij)equals the mean time on break; and redetermining Peff_(ij) if process ihas associated with it machine down time according to,

    Peff.sub.ij =(Peff.sub.ij)([MTBF.sub.ij +MTTR.sub.ij ]/MTBF.sub.ij),

where MRBF_(ij) equals the means time between machine failures andMTTR_(ij) equals the mean time to repair.
 6. The method for schedulingand operating production according to claim 3 further comprising thestep of:determining SUeff_(ij) if process i within workstation j hasassociated with it rework according to

    SUeff.sub.ij =SU.sub.ij +RSU.sub.ij,

where RSU_(ij) is the rework setup time.
 7. The method for schedulingand operating production according to claim 3 further comprising thestep of:redetermining SUeff_(ij) if process i has associated with ithuman down time according to,

    SUeff.sub.ij =(SUeff.sub.ij)([MTTB.sub.ij +MTOB.sub.ij ]/MTTB.sub.ij)

where MTTB_(ij) is the mean time to break and MTOB_(ij) is the meanstime on break.
 8. The method for scheduling and operating productionaccording to claim 3 further comprising the step of:determining thepercentage of time that the workstation is processing material.
 9. Themethod for scheduling and operating production according to claim 3further comprising the step of:determining the time during which theworkstation is down due to human down time and machine down time.
 10. Amethod for scheduling and operating a factory having a plurality ofworkstations, each workstation performs one or more processes, eachprocess operating on a batch of material, the method comprising thesteps of:determining the rate of material flow out (FLOWOUT_(ij)) ofeach process within each workstation of a factory for the factory tomeet predetermined shipping rates; determining the cycle time CT_(j) foreach workstation j to process a batch of material based upon thefollowing relationship:

    CT.sub.j =[ΣSUeff.sub.ij ]/[1-Σ(Peff.sub.ij)(FLOWOUT.sub.ij)]

where, for process i within workstation j, SUeff_(ij) is the effectiveset-up time, Peff_(ij) is effective time to process one unit ofmaterial, FLOWOUT_(ij) is the material output flow rate, and n is thenumber of processes performed by workstation j; and identifyinginefficient workstations having the longest cycle times by an evaluationof cycle times and batch sizes; and operating the processes performed bythe identified workstations having the longest cycle times to reduce thebatch size and cycle time of the identified workstations.
 11. The methodof claim 10 wherein the cycle time CT_(j) is determined when the factoryis operating at capacity to manufacture a mix products at the shippingrates.
 12. The method of claim 11 wherein the step of determining CT_(j)when the factory is operating at capacity includes the step of linearlyadjusting the flow rates such that none of the workstations areproducing at overcapacity and one or more workstations are at capacity.13. A factory for manufacturing and shipping goods, the factorycomprising:a plurality of workstations, each workstation performing oneor more processes, each process processing a batch of material havingsizes determined by a system for scheduling manufacturing processes tomeet a shipment schedule; and a system for scheduling manufacturingprocesses to meet a shipment schedule, the system including: a memoryfor storing data values of variables received from the factoryrepresenting shipping rates of a mix of products produced by thefactory, processing times per unit of material for each process in eachworkstation and set-up times for each process in each workstation; amicroprocessor, coupled tot he memory, for determining the rate ofmaterial flow out of each process within each workstation necessary forthe factory to meet the shipment schedule and for determining the sizeof the batch of material for each process of a workstation necessary tomeet each material flow rate out of each workstation for each process;and a means for providing the batch size for a process to the factory.14. A factory production scheduling and organization process, theprocess outputting information for optimizing production scheduling andorganization and improving the efficiency of a factory production lineto meet a predetermined shipping rate, the factory production linecomprised of a plurality of interconnected workstations each performingat least one process, comprising the steps of:receiving factoryproduction line organization and operation data; modelling the factoryproduction line, according to the input factory data, by generating flowrates of material through each workstation for each process andprocessing capacities of each workstation for each process to meet thepredetermined shipping rate for the production line; analyzing thefactory model according to the factory data and generated workstationmaterial flow rates and workstation processing capacities for eachprocess to evaluate workstation performance, process and production lineperformance, and identify optimal production line operating and supplycharacteristics; and operating the production line to implement theidentified optimal production line operating and supply characteristicsto substantially meet the predetermined shipping rate.
 15. The factoryproduction scheduling and organization process as in claim 14 whereinthe step of modelling the production line comprises the stepsof:determining the effective processing time per material unit producedby each workstation for each process; determining the effective set-uptime for reconfiguring the process performed by each workstation;generating, for each workstation in the production line, the flow ratesof material through each workstation necessary to meet the predeterminedshipping rate; and generating, from the material flow rates andeffective processing time for each process performed by eachworkstation, the processing capacity of each workstation for eachprocess.
 16. The factory production scheduling and organization processas in claim 15 wherein the step of analyzing the factory model comprisesthe steps of:determining material scheduling analysis for eachworkstation and process performed therein to evaluate performance of thefactory at production levels meeting the predetermined shipping rate;and determining factory capacity analysis for each workstation andprocess performed therein to evaluate performance of the factory atproduction capacity levels and identify inefficient workstations thatlimit production line output.
 17. The factory production scheduling andorganization process as in claim 16 wherein the step of determiningmaterial scheduling analysis comprises the steps of:determining thebatch size of materials needed for each process performed by eachworkstation to meet the generated material flow rates; determining thetime required to cycle between processes for each workstation; analyzingthe required batch sizes of materials for processes and cycle time forworkstations to determine optimal supply characteristics forworkstations on the production line to meet the predetermined shippingrate.
 18. The factory production scheduling and organization process asin claim 16 wherein the step of determining factory capacity analysiscomprises the steps of:analyzing the processing capacity of eachworkstation to identify the workstation operating at the highestprocessing capacity; revising the material flow rates for each processin each workstation according to the highest processing capacity so asto scale material flow rates through each workstation in the productionline to a capacity level; generating, from the revised material flowrates and effective processing times for each process performed by eachworkstation, a revised processing capacity for each workstationreflecting processing capacity when the production line is operating atthe capacity level; determining the batch size of materials needed foreach process performed by each workstation to meet the revised materialflow rates; determining the time required to cycle between processes foreach workstation; and determining the required batch sizes of materialsfor processes and cycle times for workstations operating at capacitylevel to identify inefficient workstations and processes and improvementin factory operation.
 19. A method for analyzing the scheduling andorganization of production for a factory, the method outputtinginformation on optimizing factory production scheduling and organizationto meet a predetermined shipping rate, said factory comprised of aplurality of workstations each performing at least one process,comprising the steps of:receiving factory organization and operationdata comprising workstation processing times and workstation set-uptimes for each process; modelling the factory according to the inputorganization and operation data by:(1) determining the effectiveprocessing time for each workstation to process one unit of materialaccording to a given process; (2) determining the effective set-up timerequired to configure each workstation to change between each processperformed by the workstation; (3) determining the flow rates of materialin and out of each workstation for each process required to outputmaterial from the factory meeting the predetermined shipping rate; and(4) determining the production capacity of each workstation to handlematerial flow rates in and out of each workstation for production ofmaterial units at the rate necessary to meet the predetermined shippingrate; and analyzing the production performance and efficiency of thefactory by:(1) evaluating the material flow rates through eachworkstation for each process cycle time between processes for eachworkstation to identify optimal material supply requirements for eachworkstation necessary to meet the predetermined shipping rate; (2)simulating factory operation at peak capacity levels by scaling materialflow rates through each workstation to capacity levels and evaluatingthe material flow rates and process cycle times at peak capacity levelsto identify inefficiently operating workstations and processes toestablish improvements in factory organization and operation; andoperating the plurality of workstations to implement the establishedimprovements in factory organization and operations on factoryproduction facilities.
 20. The production scheduling and organizationmethod as in claim 19 wherein:the step of determining the effectiveprocessing time comprises the step of analyzing the time required byeach workstation for each process to meet production of a set number ofmaterial units; and the step of determining the effective set-up timecomprises the step of analyzing the time required by each workstation toconfigure for production of material units.
 21. The productionscheduling and organization method as in claim 19 wherein the step ofcalculating production capacity for each workstation comprises the stepof analyzing the effective processing times and output flow rates foreach workstation and process to evaluate the production capacity of eachworkstation to process material to meet the calculated flow rate. 22.The production scheduling and organization method as in claim 21 whereinthe step of evaluating material flow rates comprises the stepsof:determining the batch size of materials needed for supplying eachworkstation according to each process performed to meet the calculatedflow rates; determining the cycle time required between processes foreach workstation; analyzing the required batch sizes of materials forprocesses and cycle times for workstations to schedule the optimalsupply of materials to each workstation on the production lien necessaryto meet the predetermined shipping rate.
 23. The production schedulingthe organization method as in claim 21 wherein the step of simulatingfactory operation at peak capacity levels comprises the stepsof:analyzing the production capacity of each workstation to identify theworkstation operating at the highest processing capacity; revising thematerial flow rates for each process in each workstation by scaling allworkstation material flow rates in accordance with the identifiedhighest production capacity; adjusting the production capacities foreach workstation according to the revised material flow rates tosimulate workstation and factory operation at a peak capacity level;determining batch sizes of materials needed for supplying eachworkstation to meet the revised flow rates for production at peakcapacity; determining the time required to cycle between processes foreach workstation; and analyzing the required batch sizes of materialsand cycle times for production at peak capacity to identify inefficientworkstations and processes and improvements in factory operation.
 24. Amethod for improving the performance of a factory having a plurality ofworkstations performing one or more processes, each process operating ona batch of material, the method comprising the steps of:determining thebatch size of material required by each workstation j to perform eachprocess i; determining a cycle time (CT_(j)) for each workstation j toprocess the material; evaluating the cycle times and batch sizes toidentify inefficient workstations and improve the processes performed byeach workstation in order to reduce the batch size and cycle time ofthat workstation; and operating the processes of the identifiedinefficient workstations to implement the improvements identified as aresult of the evaluation of the cycle times and batch sizes.